International audienceFingerprinting techniques have been proved as an effective techniques for determining the position of a mobile user in an indoor environment and in challenging environments such as mines, canyons, and tunnels where common localization techniques based on time of arrival (TOA) or received signal strength (RSS) are subject to big positioning errors. In this paper, a fingerprinting based localization technique using neural networks and ultra-wideband signals (UWB) is presented as an alternative. The fingerprinting database is built with signatures extracted from channel impulse responses (CIR) obtained by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and the adopted approach are described. Positioning performances are evaluated with different selected signatures and different sizes of the fingerprinting database
International audience— In this paper, we introduce PyLayers a new open source radio simulator built to tackle indoor localization problem. PyLayers has been designed to simulate complete dynamic scenarios including the realistic movement of persons inside a building, the transmission channel estimation for multiple radio access technologies and the position estimation relying on location-dependent parameters originated from the simulated OSI physical layer. The channel is estimated by using a fast graph-based ray tracing method. From these simulated data, location dependent parameters, such as received power or time of arrival, can be deduced. The realistic movement of persons into the building layout is modeled with a virtual forces approach. The simulated data can be directly used with one of the built-in localization algorithms or be exported to various standards extensions. Finally, the accuracies of both the channel estimation and the localization are compared to measurements and show a good match
Abstract-In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.
In this paper, we exploit the concept of data fusion in UWB (Ultra Wide Band) localization systems by using different location-dependent observables. We combine ToA (Time of Arrival) and RSS (Received Signal Strength) in order to get accurate positioning algorithms. We assume that RSS observables are usually available and we study the effect of adding ToA observables on the positioning accuracy. The proposed architecture of Hybrid Data Fusion (HDF) is based on two stages: Ranging using RSS and ToA; and Estimation of position by the fusion of estimated ranges. In the first stage, we propose a new estimator of ranges from RSS observables assuming a path loss model. In the second stage, a new ML estimator is developed to merge different ranges with different variances. In order to evaluate these algorithms, simulations are carried out in a generic indoor environment and Cramer Rao Lower Bounds (CRLB) are investigated. Those algorithms show enhanced positioning results at reasonable noise levels.
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